In Table REF , we conduct an ablation study of the uncertainty information (UI) and the proposed regularization loss function (\(L_{gen}\) ) in miniImageNet.
The baseline represents the model of ProtoNet-style [1]}, and it also takes the average of the corresponding augmented instances as the basis. UCN_A and UCN_B have the same structure and parameters as UCN, except for some differences in UI and loss functions, where UCN_A directly uses the classification score of query instances as UI.
Table REF  illustrates the effectiveness of the proposed UI.
It shows that with the help of UI, the accuracy of the experiment increases by 4.27% for the 5-way 1-shot setting.
We argue that the uncertainty of the model prediction results is high in the 5-way 1-shot setting. More beneficial instances in the query set can be selected to optimize the prototypes after taking the uncertainty information into account. Table REF  also demonstrates the effectiveness of \(L_{gen}\) , especially on 5-way 1-shot setting.
